Comparing species distribution models constructed with different subsets of environmental predictors

MaxEnt Modeling and Effects of Climate Change on Shifts in Habitat Suitability for Sorbus alnifolia in China

Anthropogenic climate change stands out as one of the primary forces expected to reshape Earth's ecosystems and global biodiversity in the coming decades. Sorbus alnifolia, which occurs in deciduous forests, is valued for its ornamental appeal and practical uses but is reported to be declining in the wild. Nevertheless, the distribution of this species' suitable range, along with the key ecological and environmental drivers that shape its habitat suitability, remains largely unknown. By analyzing 198 occurrence records and 54 environmental factors, we employed MaxEnt to project S. alnifolia's current and future habitat suitability. Our results showed that annual precipitation (37.4%), normalized difference vegetation index (30.0%), August water vapor pressure (20.8%), and temperature annual range (3.4%) were the most significant variables explaining S. alnifolia's environmental requirements. The suitable habitats were primarily scattered across eastern and central China. Under projected future climatic conditions, the total expanse of potential habitat is expected to increase. However, most of this expansion involves low-suitability habitats, whereas moderately and highly suitable habitats are likely to shrink, especially in southern and lower-altitude regions of China. Based on these findings, we propose several conservation strategies to support the long-term sustainability of S. alnifolia.

Habitat Suitability Assessment of Key Wildlife in Hainan Tropical Rainforest Based on ESDM

Hainan tropical rainforest is the largest contiguous tropical rainforest in China, but it has experienced increasing disturbances from anthropogenic activities in recent decades due to economic and social development. However, the current status of wildlife habitats within the rainforest remains insufficiently studied, lacking systematic and scientific assessments necessary to guide effective biodiversity conservation strategies. This study focuses on Jianfengling area of Hainan tropical rainforest, using wildlife infrared camera monitoring data and habitat environmental factor data collected through multi-source monitoring in 2020-2021. By applying the Ensemble Species Distribution Model (ESDM), we assessed the spatial distribution of habitat suitability and its influencing factors for seven representative wildlife species, as well as the overall spatial distribution of multi-species habitat suitability. The results indicate that wildlife habitat suitability in Jianfengling study area exhibits a spatial pattern of high suitability in the central regions and low suitability in surrounding areas. Anthropogenic activities and DEM were identified as the most significant factors influencing habitat selection, with most species favoring mid and high altitude areas (500-1000 m) where human activities are less prevalent. This study provides scientific support for tropical rainforest management authorities to optimize resource allocation, develop dynamic monitoring strategies, and implement effective conservation measures.

Assessing tick attachments to humans with citizen science data: spatio-temporal mapping in Switzerland from 2015 to 2021 using spatialMaxent

BACKGROUND

Ticks are the primary vectors of numerous zoonotic pathogens, transmitting more pathogens than any other blood-feeding arthropod. In the northern hemisphere, tick-borne disease cases in humans, such as Lyme borreliosis and tick-borne encephalitis, have risen in recent years, and are a significant burden on public healthcare systems. The spread of these diseases is further reinforced by climate change, which leads to expanding tick habitats. Switzerland is among the countries in which tick-borne diseases are a major public health concern, with increasing incidence rates reported in recent years.

METHODS

In response to these challenges, the "Tick Prevention" app was developed by the Zurich University of Applied Sciences and operated by A&K Strategy Ltd. in Switzerland. The app allows for the collection of large amounts of data on tick attachment to humans through a citizen science approach. In this study, citizen science data were utilized to map tick attachment to humans in Switzerland at a 100 m spatial resolution, on a monthly basis, for the years 2015 to 2021. The maps were created using a state-of-the-art modeling approach with the software extension spatialMaxent, which accounts for spatial autocorrelation when creating Maxent models.

RESULTS

Our results consist of 84 maps displaying the risk of tick attachments to humans in Switzerland, with the model showing good overall performance, with median AUC ROC values ranging from 0.82 in 2018 to 0.92 in 2017 and 2021 and convincing spatial distribution, verified by tick experts for Switzerland. Our study reveals that tick attachment to humans is particularly high at the edges of settlement areas, especially in sparsely built-up suburban regions with green spaces, while it is lower in densely urbanized areas. Additionally, forested areas near cities also show increased risk levels.

CONCLUSIONS

This mapping aims to guide public health interventions to reduce human exposure to ticks and to inform the resource planning of healthcare facilities. Our findings suggest that citizen science data can be valuable for modeling and mapping tick attachment risk, indicating the potential of citizen science data for use in epidemiological surveillance and public healthcare planning.

The role of river connectivity in the distribution of fish in an anthropized watershed

The ongoing biodiversity crisis is especially severe in freshwater habitats. Anthropized watersheds, such as the Seine-Normandie basin in France, are particularly affected by human interference. The study of fish species distribution in watersheds often relies on environmental drivers such as land use or climate. Yet, fish are also exposed to river connectivity constraints, such as dams, that are understudied despite their potential impact on fish dispersal. For this study, we investigated the role of local and whole-basin longitudinal connectivity in fish distribution. We designed connectivity indices based on river network characteristics and specific mobility for 33 species and included these indices in species distribution models, taking into account habitat suitability, to quantify their role in species distribution. Keeping the best index for each species, an average of 29 % - and up to 57 % - of explained fish distribution, depending on species, was tied to connectivity. We found that high connectivity often had a significant and positive linear effect on species presence probability. Using a scoring system across multiple indices, we found connectivity indices that took local context into account (e.g. the ecological zonation of the river) performed consistently better than others. Indices that took only dispersal limitation into account scored higher for 12 species, while barriers, alone, were the most important constraint for 10 species, the remaining 11 being associated with both. This work points to fragmentation as a cause for lower likelihood of presence for many non-diadromous river fish species. It highlights the importance of considering both physical and functional connectivity constraints in fish distribution and provides additional insights for river management and restoration.

The Anthropocene and the biodiversity crisis: an eco-evolutionary perspective

A major facet of the Anthropocene is global change, such as climate change, caused by human activities, which drastically affect biodiversity with all-scale declines and homogenization of biotas. This crisis does not only affect the ecological dynamics of biodiversity, but also its evolutionary dynamics, including genetic diversity, an aspect that is generally neglected. My tenet is therefore to consider biodiversity dynamics from an eco-evolutionary perspective, i.e. explicitly accounting for the possibility of rapid evolution and its feedback on ecological processes and the environment. I represent the impact of the various avatars of global change in a temporal perspective, from pre-industrial time to the near future, allowing to visualize their dynamics and to set desired values that should not be trespassed for a given time (e.g., +2 °C for 50 years from now). After presenting the impact of various stressors (e.g., climate change) on biodiversity, this representation is used to heuristically show the relevance of an eco-evolutionary perspective: (i) to analyze how biodiversity will respond to the stressors, for example by seeking out more suitable conditions or adapting to new conditions; (ii) to serve in predictive exercises to envision future dynamics (decades to centuries) under stressor impact; (iii) to propose nature-based solutions to the crisis. Significant obstacles stand in the way of the development of such an approach, in particular the general lack of interest in intraspecific diversity, and perhaps more generally a lack of understanding that, we, humans, are only a modest part of biodiversity.

Impact of climate change on Colombian Pacific coast mangrove bivalves distribution

The mangrove bivalves, Anadara tuberculosa and Anadara similis, are pivotal for the Colombian Pacific coast mangrove ecosystems and economies. In this study, the current and future potential distribution of these bivalves is modeled considering climate change. The future models (2030 and 2050) were projected considering the new climate scenarios (SSP1, SSP2, and SSP5) proposed by the IPCC in its sixth report. Our findings reveal areas in the Colombian Pacific coast, notably Nariño, Cauca, southern Valle del Cauca, and Chocó, with high environmental suitability for these bivalves. However, the 2050 projections, especially under the pessimistic SSP5 scenario, indicate potential adverse impacts from climate change. By 2030 and 2050, the species might lean more toward a southwesterly distribution in the Colombian Pacific coast. Climate-induced spatiotemporal mismatches could occur between the bivalves and the mangroves in some areas. These insights are crucial for effective conservation and management strategies for these species.

Gaps and opportunities in modelling human influence on species distributions in the Anthropocene

Understanding species distributions is a global priority for mitigating environmental pressures from human activities. Ample studies have identified key environmental (climate and habitat) predictors and the spatial scales at which they influence species distributions. However, regarding human influence, such understandings are largely lacking. Here, to advance knowledge concerning human influence on species distributions, we systematically reviewed species distribution modelling (SDM) articles and assessed current modelling efforts. We searched 12,854 articles and found only 1,429 articles using human predictors within SDMs. Collectively, these studies of >58,000 species used 2,307 unique human predictors, suggesting that in contrast to environmental predictors, there is no 'rule of thumb' for human predictor selection in SDMs. The number of human predictors used across studies also varied (usually one to four per study). Moreover, nearly half the articles projecting to future climates held human predictors constant over time, risking false optimism about the effects of human activities compared with climate change. Advances in using human predictors in SDMs are paramount for accurately informing and advancing policy, conservation, management and ecology. We show considerable gaps in including human predictors to understand current and future species distributions in the Anthropocene, opening opportunities for new inquiries. We pose 15 questions to advance ecological theory, methods and real-world applications.

The devil is in the detail: Environmental variables frequently used for habitat suitability modeling lack information for forest-dwelling bats in Germany

In response to the pressing challenges of the ongoing biodiversity crisis, the protection of endangered species and their habitats, as well as the monitoring of invasive species are crucial. Habitat suitability modeling (HSM) is often treated as the silver bullet to address these challenges, commonly relying on generic variables sourced from widely available datasets. However, for species with high habitat requirements, or for modeling the suitability of habitats within the geographic range of a species, variables at a coarse level of detail may fall short. Consequently, there is potential value in considering the incorporation of more targeted data, which may extend beyond readily available land cover and climate datasets. In this study, we investigate the impact of incorporating targeted land cover variables (specifically tree species composition) and vertical structure information (derived from LiDAR data) on HSM outcomes for three forest specialist bat species (Barbastella barbastellus, Myotis bechsteinii, and Plecotus auritus) in Rhineland-Palatinate, Germany, compared to commonly utilized environmental variables, such as generic land-cover classifications (e.g., Corine Land Cover) and climate variables (e.g., Bioclim). The integration of targeted variables enhanced the performance of habitat suitability models for all three bat species. Furthermore, our results showed a high difference in the distribution maps that resulted from using different levels of detail in environmental variables. This underscores the importance of making the effort to generate the appropriate variables, rather than simply relying on commonly used ones, and the necessity of exercising caution when using habitat models as a tool to inform conservation strategies and spatial planning efforts.

The great urban shift: Climate change is predicted to drive mass species turnover in cities

Human experiences with nature are important for our culture, economy, and health. Anthropogenically-driven climate change is causing widespread shifts in biodiversity and resident urban wildlife are no exception. We modelled over 2,000 animal species to predict how climate change will impact terrestrial wildlife within 60 Canadian and American cities. We found evidence of an impending great urban shift where thousands of species will disappear across the selected cities, being replaced by new species, or not replaced at all. Effects were largely species-specific, with the most negatively impacted taxa being amphibians, canines, and loons. These predicted shifts were consistent across scenarios of greenhouse gas emissions, but our results show that the severity of change will be defined by our action or inaction to mitigate climate change. An impending massive shift in urban wildlife will impact the cultural experiences of human residents, the delivery of ecosystem services, and our relationship with nature.

Distribution and protection of Thesium chinense Turcz. under climate and land use change

Wild medicinal plants are prominent in the field of Traditional Chinese Medicine (TCM), but their availability is being impacted by human activities and ecological degradation in China. To ensure sustainable use of these resources, it is crucial to scientifically plan areas for wild plant cultivation. Thesium chinense, a known plant antibiotic, has been overharvested in recent years, resulting in a sharp reduction in its wild resources. In this study, we employed three atmospheric circulation models and four socio-economic approaches (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) to investigate the primary environmental factors influencing the distribution of T. chinense. We also examined changes in its suitable area using the Biomod2 package. Additionally, we utilized the PLUS model to project and analyze future land use changes in climate-stable regions for T. chinense. Our planning for wild tending areas of T. chinense was facilitated by the ZONATION software. Over the next century, the climate-stable regions for T. chinense in China is approximately 383.05 × 104 km2, while the natural habitat in this region will progressively decline. Under the current climate conditions, about 65.06% of the habitats in the high suitable areas of T. chinense are not affected by future land use changes in China. Through hotspot analysis, we identified 17 hotspot cities as ideal areas for the wild tending of T. chinense, including 6 core hotspot cities, 6 sub-hotspot cities, and 5 fringe hotspot cities. These findings contribute to a comprehensive research framework for the cultivation planning of T. chinense and other medicinal plants.

Constructing indicator species distribution models to study the potential invasion risk of invasive plants: A case of the invasion of Parthenium hysterophorus in China

Aim

As invasive plants are often in a non-equilibrium expansion state, traditional species distribution models (SDMs) are likely underestimating their suitable habitat. New methods are necessary to identify potential invasion risk areas.

Location

Tropical monsoon rainforest and subtropical evergreen broad-leaved forest regions in China.

Methods

We took Parthenium hysterophorus as a case study to predict its potential invasion risk using climate, terrain, and human activity variables. First, a generalized joint attribute model (GJAM) was constructed using the occurrence of P. hysterophorus and its 27 closely related species in Taiwan, given it is widely distributed in Taiwan. Based on the output correlation values, two positively correlated species (Cardiospermum halicacabum and Portulaca oleracea) and one negatively correlated species (Crassocephalum crepidioides) were selected as indicator species. Second, the distributions of P. hysterophorus and its indicator species in the study area were predicted separately using an ensemble model (EM). Third, when selecting indicator species to construct indicator SDMs, two treatments (indicator species with positive correlation only, or both positive and negative correlation) were considered. The indicator species' EM predictions were overlaid using a weighted average method, and a better indicator SDMs prediction result was selected by comparison. Finally, the EM prediction result of P. hysterophorus was used to optimize the indicator SDMs result by a maximum overlay.

Results

The optimized indicator SDMs prediction showed an expanded range beyond the current geographic range compared to EM and the thresholds for predicting key environmental variables were wider. It also reinforced the human activities' influence on the potential distribution of P. hysterophorus.

Main Conclusions

For invasive plants with expanding ranges, information about indicator species distribution can be borrowed as a barometer for areas not currently invaded. The optimized indicator SDMs allow for more efficient potential invasion risk prediction. On this basis, invasive plants can be prevented earlier.

Niche Filling Dynamics of Ragweed (Ambrosia artemisiifolia L.) during Global Invasion

Determining whether the climatic ecological niche of an invasive alien plant is similar to that of the niche occupied by its native population (ecological niche conservatism) is essential for predicting the plant invasion process. Ragweed (Ambrosia artemisiifolia L.) usually poses serious threats to human health, agriculture, and ecosystems within its newly occupied range. We calculated the overlap, stability, unfilling, and expansion of ragweed's climatic ecological niche using principal component analysis and performed ecological niche hypothesis testing. The current and potential distribution of A. artemisiifolia was mapped by ecological niche models to identify areas in China with the highest potential risk of A. artemisiifolia invasion. The high ecological niche stability indicates that A. artemisiifolia is ecologically conservative during the invasion. Ecological niche expansion (expansion = 0.407) occurred only in South America. In addition, the difference between the climatic and native niches of the invasive populations is mainly the result of unpopulated niches. The ecological niche model suggests that southwest China, which has not been invaded by A. artemisiifolia, faces an elevated risk of invasion. Although A. artemisiifolia occupies a climatic niche distinct from native populations, the climatic niche of the invasive population is only a subset of the native niche. The difference in climatic conditions is the main factor leading to the ecological niche expansion of A. artemisiifolia during the invasion. Additionally, human activities play a substantial role in the expansion of A. artemisiifolia. Alterations in the A. artemisiifolia niche would help explain why this species is so invasive in China.

Microhabitat modeling of the invasive Asian longhorned tick (Haemaphysalis longicornis) in New Jersey, USA

The Asian longhorned tick (Haemaphysalis longicornis) is a vector of multiple arboviral and bacterial pathogens in its native East Asia and expanded distribution in Australasia. This species has both bisexual and parthenogenetic populations that can reach high population densities under favorable conditions. Established populations of parthenogenetic H. longicornis were detected in the eastern United States in 2017 and the possible range of this species at the continental level (North America) based on climatic conditions has been modeled. However, little is known about factors influencing the distribution of H. longicornis at geographic scales relevant to local surveillance and control. To examine the importance of local physiogeographic conditions such as geology, soil characteristics, and land cover on the distribution of H. longicornis we employed ecological niche modeling using three machine learning algorithms - Maxent, Random Forest (RF), and Generalized Boosting Method (GBM) to estimate probability of finding H. longicornis in a particular location in New Jersey (USA), based on environmental predictors. The presence of H. longicornis in New Jersey was positively associated with Piedmont physiogeographic province and two soil types - Alfisols and Inceptisols. Soil hydraulic conductivity was the most important predictor explaining H. longicornis habitat suitability, with more permeable sandy soils with higher hydraulic conductivity being less suitable than clay or loam soils. The models were projected over the state of New Jersey creating a probabilistic map of H. longicornis habitat suitability at a high spatial resolution of 90×90 meters. The model's sensitivity was 87% for locations sampled in 2017-2019 adding to the growing evidence of the importance of soil characteristics to the survival of ticks. For the 2020-2022 dataset the model fit was 57%, suggestive of spillover to less optimal habitats or, alternatively, heterogeneity in soil characteristics at the edges of broad physiographic zones. Further modeling should incorporate abundance and life-stage information as well as detailed characterization of the soil at collection sites. Once critical parameters that drive the survival and abundance of H. longicornis are identified they can be used to guide surveillance and control strategies for this invasive species.

Between the Cape Fold Mountains and the deep blue sea: Comparative phylogeography of selected codistributed ectotherms reveals asynchronous cladogenesis

We compare the phylogeographic structure of 13 codistributed ectotherms including four reptiles (a snake, a legless skink and two tortoise species) and nine invertebrates (six freshwater crabs and three velvet worm species) to test the presence of congruent evolutionary histories. Phylogenies were estimated and dated using maximum likelihood and Bayesian methods with combined mitochondrial and nuclear DNA sequence datasets. All taxa demonstrated a marked east/west phylogeographic division, separated by the Cape Fold Mountain range. Phylogeographic concordance factors were calculated to assess the degree of evolutionary congruence among the study species and generally supported a shared pattern of diversification along the east/west longitudinal axis. Testing simultaneous divergence between the eastern and western phylogeographic regions indicated pseudocongruent evolutionary histories among the study taxa, with at least three separate divergence events throughout the Mio/Plio/Pleistocene epochs. Climatic refugia were identified for each species using climatic niche modelling, demonstrating taxon-specific responses to climatic fluctuations. Climate and the Cape Fold Mountain barrier explained the highest proportion of genetic diversity in all taxa, while climate was the most significant individual abiotic variable. This study highlights the complex interactions between the evolutionary history of fauna, the Cape Fold Mountains and past climatic oscillations during the Mio/Plio/Pleistocene. The congruent east/west phylogeographic division observed in all taxa lends support to the conclusion that the longitudinal climatic gradient within the Greater Cape Floristic Region, mediated in part by the barrier to dispersal posed by the Cape Fold Mountains, plays a major role in lineage diversification and population differentiation.

Climate change threatens the distribution of major woody species and ecosystem services provision in southern Africa

In southern Africa, woody vegetation provides essential ecological, regulation, and cultural ecosystem services (ES), yet many species and ecosystems are increasingly threatened by climate change and land-use transformations. We investigated the effect of climate change on the distribution of eight species in 18 countries in southern Africa, covering 36% of the continent. We proposed a loser/winner ranking of the species based on the changes in land climatic suitability within their historical distribution and future gains and losses of suitable areas. We interpreted these findings in terms of changes in key ES (timber, food, and energy) provision and identified hotspots of ES provision decline. We used species presence data from the Global Biodiversity Information Facility, climatic data from the AfriClim dataset, and the MaxEnt algorithm to project the changes in species-specific land climatic suitability. Among the eight investigated species, the baseline suitability range of Mopane (Colophosperm mopane) was least affected by climate change. At the same time, the area of its future distribution was projected to double, rendering it a regional winner. Another two species, manketti (Schinziophyton rautanenii) and leadwood (Combretum imberbe) showed high future gains too; however, the impact on their baseline suitability range differed between the climatic scenarios. The baseline range of African rosewood (Guibourtia coleosperma) declined entirely, and the future gains were negligible, rendering the species a regional loser. The effect of climate change was particularly severe on timber-producing species (four out of eight species), while species providing food (four species) and energy (four species) were affected less. Our projections portrayed distinct hotspot and coldspot areas, where climatic suitability for multiple species was concurrently projected to decline or persist. This assessment can inform spatially targeted adaptation and conservation actions and strategies, which are currently lacking in many African regions.

Understanding the temporal dynamics of estimated environmental niche hypervolumes for marine fishes

Understanding how species respond to the environment is essential in ecology, evolution, and conservation. Abiotic factors can influence species responses and the multi-dimensional space of abiotic factors that allows a species to grow represents the environmental niche. While niches are often assumed to be constant and robust, they are most likely changing over time and estimation can be influenced by population biology, sampling intensity, and computation methodology. Here, we used a 12-year time series of survey data to fit annual ecological niche models (ENMs) for 10 marine fish species by using two regression and two machine learning algorithms to evaluate the variation and differentiation of environmental niches. Fitted ENMs were used to develop multi-dimensional annual and pooled hypervolumes that were evaluated over time and across ENM algorithms, species, and years by computing volume, distance, and dissimilarity metrics for each annual estimated niche. We then investigated potential drivers of estimated hypervolume dynamics including species abundance, species occurrence, sampling effort, salinity, red tides severity, and algorithm. Overall, our results revealed that estimated niches varied over time and across ENM, species, and algorithms. Niche estimation was influenced over time by multiple factors suggesting high complexity on niche dynamics interpretation. Species with high occurrence tended to have a closer representation of the pooled niche and years with higher abundance tended to produce niche expansion. ENM algorithm, sampling effort, seawater salinity, and red tides explained the deviations from the pooled niche. Greater sampling effort led to more comprehensive and complete estimates of species niches. High red tides severity triggered niche contraction. Our results emphasize the predictable effects of population, sampling, and environment on species niche estimation and interpretation, and that each should be considered when performing and interpreting ecological niche analyses. Our niche analysis approach may contribute to effectively quantifying and assessing niche dynamics.

Comparison between optimized MaxEnt and random forest modeling in predicting potential distribution: A case study with Quasipaa boulengeri in China

Random forest (RF) and MaxEnt models are shallow machine learning approaches that perform well in predicting species' potential distributions. RF models can produce robust results with the default automatic configuration in most cases, but it is necessary for MaxEnt to optimize the model settings to improve the performance, and the predictive performance difference between optimized MaxEnt and RF is uncertain. To explore this issue, the potential distribution of the endangered amphibian Quasipaa boulengeri in China was predicted using optimized MaxEnt and RF models. A total of 408 occurrence data were selected, 1000 locations were generated as pseudo-absence data by the geographic distance method, and 10,000 sites were selected as background data by creating a bias file. Partial ROC at different thresholds and success rate curves were used to compare the predictive performances between optimized MaxEnt and RF. Our results showed that the RF and optimized MaxEnt models both had good performance in predicting the potential distribution of Q. boulengeri, with the RF model performing slightly better whether based on partial ROC or success rate curves. Furthermore, the core suitable habitat regions of Q. boulengeri identified by RF and MaxEnt were similar and were all located in the Sichuan, Chongqing, Hubei, Hunan, and Guizhou provinces. However, the RF model produced a habitat suitability map with higher discrimination and greater heterogeneity. Temperature annual range, mean temperature of the driest quarter, and annual precipitation were the vital environmental variables limiting the distribution of Q. boulengeri. The RF model is the stronger machine learner. We believe it may be more applicable in predicting the native potential distributions of species with sufficient occurrence data, given the additional predictive detail, the simplicity of use, the computational time involved, and the operational complexity.

Global warming pushes the distribution range of the two alpine 'glasshouse' Rheum species north- and upwards in the Eastern Himalayas and the Hengduan Mountains

Alpine plants' distribution is being pushed higher towards mountaintops due to global warming, finally diminishing their range and thereby increasing the risk of extinction. Plants with specialized 'glasshouse' structures have adapted well to harsh alpine environments, notably to the extremely low temperatures, which makes them vulnerable to global warming. However, their response to global warming is quite unexplored. Therefore, by compiling occurrences and several environmental strata, we utilized multiple ensemble species distribution modeling (eSDM) to estimate the historical, present-day, and future distribution of two alpine 'glasshouse' species Rheum nobile Hook. f. & Thomson and R. alexandrae Batalin. Rheum nobile was predicted to extend its distribution from the Eastern Himalaya (EH) to the Hengduan Mountains (HM), whereas R. alexandrae was restricted exclusively in the HM. Both species witnessed a northward expansion of suitable habitats followed by a southerly retreat in the HM region. Our findings reveal that both species have a considerable range shift under different climate change scenarios, mainly triggered by precipitation rather than temperature. The model predicted northward and upward migration for both species since the last glacial period which is mainly due to expected future climate change scenarios. Further, the observed niche overlap between the two species presented that they are more divergent depending on their habitat, except for certain regions in the HM. However, relocating appropriate habitats to the north and high elevation may not ensure the species' survival, as it needs to adapt to the extreme climatic circumstances in alpine habitats. Therefore, we advocate for more conservation efforts in these biodiversity hotspots.

Predicting the current and future risk of ticks on livestock farms in Britain using random forest models

The most abundant tick species in northern Europe, Ixodes ricinus, transmits a range of pathogens that cause disease in livestock. As I. ricinus distribution is influenced by climate, tick-borne disease risk is expected to change in the future. The aims of this work were to build a spatial model to predict current and future risk of ticks on livestock farms across Britain. Variables relating both to tick hazard and livestock exposure were included, to capture a niche which may be missed by broader scale models. A random forest machine learning model was used due to its ability to cope with correlated variables and interactions. Data on tick presence and absence on sheep and cattle farms was obtained from a retrospective questionnaire survey of 926 farmers. The ROC of the final model was 0.80. The model outputs matched observed patterns of tick distribution, with areas of highest tick risk in southwest and northwest England, Wales, and west Scotland. Overall, the probability of tick presence on livestock farms was predicted to increase by 5-7 % across Britain under future climate scenarios. The predicted increase is greater at higher altitudes and latitudes, further increasing the risk of tick-borne disease on farms in these areas.

Forest tree species distribution for Europe 2000-2020: mapping potential and realized distributions using spatiotemporal machine learning

This article describes a data-driven framework based on spatiotemporal machine learning to produce distribution maps for 16 tree species (Abies alba Mill., Castanea sativa Mill., Corylus avellana L., Fagus sylvatica L., Olea europaea L., Picea abies L. H. Karst., Pinus halepensis Mill., Pinus nigra J. F. Arnold, Pinus pinea L., Pinus sylvestris L., Prunus avium L., Quercus cerris L., Quercus ilex L., Quercus robur L., Quercus suber L. and Salix caprea L.) at high spatial resolution (30 m). Tree occurrence data for a total of three million of points was used to train different algorithms: random forest, gradient-boosted trees, generalized linear models, k-nearest neighbors, CART and an artificial neural network. A stack of 305 coarse and high resolution covariates representing spectral reflectance, different biophysical conditions and biotic competition was used as predictors for realized distributions, while potential distribution was modelled with environmental predictors only. Logloss and computing time were used to select the three best algorithms to tune and train an ensemble model based on stacking with a logistic regressor as a meta-learner. An ensemble model was trained for each species: probability and model uncertainty maps of realized distribution were produced for each species using a time window of 4 years for a total of six distribution maps per species, while for potential distributions only one map per species was produced. Results of spatial cross validation show that the ensemble model consistently outperformed or performed as good as the best individual model in both potential and realized distribution tasks, with potential distribution models achieving higher predictive performances (TSS = 0.898, R2 logloss = 0.857) than realized distribution ones on average (TSS = 0.874, R2 logloss = 0.839). Ensemble models for Q. suber achieved the best performances in both potential (TSS = 0.968, R2 logloss = 0.952) and realized (TSS = 0.959, R2 logloss = 0.949) distribution, while P. sylvestris (TSS = 0.731, 0.785, R2 logloss = 0.585, 0.670, respectively, for potential and realized distribution) and P. nigra (TSS = 0.658, 0.686, R2 logloss = 0.623, 0.664) achieved the worst. Importance of predictor variables differed across species and models, with the green band for summer and the Normalized Difference Vegetation Index (NDVI) for fall for realized distribution and the diffuse irradiation and precipitation of the driest quarter (BIO17) being the most frequent and important for potential distribution. On average, fine-resolution models outperformed coarse resolution models (250 m) for realized distribution (TSS = +6.5%, R2 logloss = +7.5%). The framework shows how combining continuous and consistent Earth Observation time series data with state of the art machine learning can be used to derive dynamic distribution maps. The produced predictions can be used to quantify temporal trends of potential forest degradation and species composition change.

A comparison in species distribution model performance of succulents using key species and subsets of environmental predictors

Identifying the environmental drivers of the global distribution of succulent plants using the Crassulacean acid metabolism pathway of photosynthesis has previously been investigated through ensemble-modeling of species delimiting the realized niche of the natural succulent biome. An alternative approach, which may provide further insight into the fundamental niche of succulent plants in the absence of dispersal limitation, is to model the distribution of selected species that are globally widespread and have become naturalized far beyond their native habitats. This could be of interest, for example, in defining areas that may be suitable for cultivation of alternative crops resilient to future climate change. We therefore explored the performance of climate-only species distribution models (SDMs) in predicting the drivers and distribution of two widespread CAM plants, Opuntia ficus-indica and Euphorbia tirucalli. Using two different algorithms and five predictor sets, we created distribution models for these exemplar species and produced an updated map of global inter-annual rainfall predictability. No single predictor set produced markedly more accurate models, with the basic bioclim-only predictor set marginally out-performing combinations with additional predictors. Minimum temperature of the coldest month was the single most important variable in determining spatial distribution, but additional predictors such as precipitation and inter-annual precipitation variability were also important in explaining the differences in spatial predictions between SDMs. When compared against previous projections, an a posteriori approach correctly does not predict distributions in areas of ecophysiological tolerance yet known absence (e.g., due to biotic competition). An updated map of inter-annual rainfall predictability has successfully identified regions known to be depauperate in succulent plants. High model performance metrics suggest that the majority of potentially suitable regions for these species are predicted by these models with a limited number of climate predictors, and there is no benefit in expanding model complexity and increasing the potential for overfitting.

Predicting Potential Spawning Habitat by Ensemble Species Distribution Models: The Case Study of European Anchovy (Engraulis encrasicolus) in the Strait of Sicily

Species distribution models (SDMs) are important tools for exploring the complex association between species and habitats. Here, we applied six SDMs combining 1946 pieces of presence/absence data regarding European anchovy eggs with environmental parameters from surveys conducted in the Strait of Sicily from 1998 to 2016. We aimed to investigate the mechanisms influencing spawning habitat suitability for anchovy (Engraulis encrasicolus). The dataset was split into a training subset (75%) and a test subset (25%) for evaluating the predictive performance of the models. The results suggested the role of environmental parameters in explaining egg occurrence, model accuracy and spatial predictions. Bottom depth consistently had the highest importance, followed by absolute dynamic topography, which gives insights about local mesoscale oceanographic features. Each modelling method, except the linear model, produced successful performance for both the training and the test datasets. The spatial predictions were estimated as weighted averages of single-model predictions, with weights based on discriminatory power measured by the area under the receiver operating characteristic curve (AUC). This ensemble approach often provided more robust predictions than a single model. The coastal waters were identified as the most favorable for anchovy spawning, especially the south-central sector and the area around the southern-most tip of Sicily.

Understanding Species–Habitat Associations: A Case Study with the World’s Bears

Habitat modeling is one of the most common practices in ecology today, aimed at understanding complex associations between species and an array of environmental, bioclimatic, and anthropogenic factors. This review of studies of seven species of terrestrial bears (Ursidae) occupying four continents examines how habitat models have been employed, and the functionality of their predictions for management and conservation. Bear occurrence data have been obtained at the population level, as presence points (e.g., sign surveys or camera trapping), or as locations of individual radio-collared animals. Radio-collars provide greater insights into how bears interact with their environment and variability within populations; they are more commonly used in North America and Europe than in South America and Asia. Salient problematic issues apparent from this review included: biases in presence data; predictor variables being poor surrogates of actual behavioral drivers; predictor variables applied at a biologically inappropriate scale; and over-use of data repositories that tend to detach investigators from the species. In several cases, multiple models in the same area yielded different predictions; new presence data occurred outside the range of predicted suitable habitat; and future range projections, based on where bears presently exist, underestimated their adaptability. Findings here are likely relevant to other taxa.

Ensemble ecological niche modeling of West Nile virus probability in Florida

Ecological Niche Modeling is a process by which spatiotemporal, climatic, and environmental data are analyzed to predict the distribution of an organism. Using this process, an ensemble ecological niche model for West Nile virus habitat prediction in the state of Florida was developed. This model was created through the weighted averaging of three separate machine learning models—boosted regression tree, random forest, and maximum entropy—developed for this study using sentinel chicken surveillance and remote sensing data. Variable importance differed among the models. The highest variable permutation value included mean dewpoint temperature for the boosted regression tree model, mean temperature for the random forest model, and wetlands focal statistics for the maximum entropy mode. Model validation resulted in area under the receiver curve predictive values ranging from good [0.8728 (95% CI 0.8422–0.8986)] for the maximum entropy model to excellent [0.9996 (95% CI 0.9988–1.0000)] for random forest model, with the ensemble model predictive value also in the excellent range [0.9939 (95% CI 0.9800–0.9979]. This model should allow mosquito control districts to optimize West Nile virus surveillance, improving detection and allowing for a faster, targeted response to reduce West Nile virus transmission potential.

Using climatic variables alone overestimate climate change impacts on predicting distribution of an endemic species

Plant species distribution is constrained by both dynamic and static environmental variables. However, relative contribution of dynamic and static variables in determining species distributions is not clear and has far reaching implications for range change dynamics in a changing world. Prunus eburnea (Spach) Aitch. & Hemsl. is an endemic and medicinal plant species of Iran. It has rendered itself as ecologically important for its functions and services and is currently in need of habitat conservation measures requiring investigation of future potential distribution range. We conducted sampling of 500 points that cover most of Iran plateau and recorded the P. eburnea presence and absence during the period 2015-2017. In this study, we evaluated impacts of using only climatic variables versus combined with topographic and edaphic variables on accuracy criteria and predictive ability of current and future habitat suitability of this species under climate change (CCSM4, RCP 2.6 in 2070) by generalized linear model and generalized boosted model. Models' performances were evaluated using area under the curve, sensitivity, specificity and the true skill statistic. Then, we evaluated here, driving environmental variables determining the distribution of P. eburnea by using principal component analysis and partitioning methods. Our results indicated that prediction with high accuracy of the spatial distribution of P. eburnea requires both climate information, as dynamic primary factors, but also detailed information on soil and topography variables, as static factors. The results emphasized that environmental variable grouping influenced the modelling prediction ability strongly and the use of only climate variables would exaggerate the predicted distribution range under climate change. Results supported using both dynamic and static variables improved accuracy of the modeling and provided more realistic prediction of species distribution under influence of climate change.

Is the southern crab Halicarcinus planatus (Fabricius, 1775) the next invader of Antarctica?

The potential for biological colonization of Antarctic shores is an increasingly important topic in the context of anthropogenic warming. Successful Antarctic invasions to date have been recorded exclusively from terrestrial habitats. While non-native marine species such as crabs, mussels and tunicates have already been reported from Antarctic coasts, none have as yet established there. Among the potential marine invaders of Antarctic shallow waters is Halicarcinus planatus (Fabricius, 1775), a crab with a circum-Subantarctic distribution and substantial larval dispersal capacity. An ovigerous female of this species was found in shallow waters of Deception Island, South Shetland Islands in 2010. A combination of physiological experiments and ecological modelling was used to assess the potential niche of H. planatus and estimate its future southward boundaries under climate change scenarios. We show that H. planatus has a minimum thermal limit of 1°C, and that its current distribution (assessed by sampling and niche modelling) is physiologically restricted to the Subantarctic region. While this species is presently unable to survive in Antarctica, future warming under both 'strong mitigation' and 'no mitigation' greenhouse gas emission scenarios will favour its niche expansion to the Western Antarctic Peninsula (WAP) by 2100. Future human activity also has potential to increase the probability of anthropogenic translocation of this species into Antarctic ecosystems.

The environmental context of inducible HSP70 expression in Eastern Brook Trout

Much research has focused on the population-level effects of climate change on Eastern Brook Trout (Salvelinus fontinalis). While some studies have considered here sub-lethal stress caused by warming waters, the role of multiple, interacting stressors remains largely unexplored. We used inducible heat shock protein 70 (HSP70) as a molecular biomarker to assess in situ response of Eastern Brook Trout in headwater streams to multiple potential stressors, including temperature. Over 7 sampling events during 2018 and 2019, we sampled 141 fish and found that HSP70 expression and 3-day mean water temperature exhibited a quadratic relationship (R ²-adj = 0.68). Further analyses showed that HSP70 expression was explained by temperature, relative water level and their interaction (R ²-adj = 0.75), while fish size and capture location were not factors. We observed a significant increase in HSP70 expression during periods of low relative water level with warm temperatures (~18°C) and also during high relative water level with cold temperatures (~8°C). Our results suggest that temperatures at the edges of the preferred range coupled with relative water level might act together to trigger the cellular stress response in Eastern Brook Trout and that there is greater variation in response at colder temperatures. These findings reinforce the need to consider complex, interactive stressors in influencing the health and persistence of Eastern Brook Trout populations into the future.

Determinants of habitat suitability models transferability across geographically disjunct populations: Insights from Vipera ursinii urs inii

Transferability of habitat suitability models (HSMs), essential to accurately predict outside calibration conditions, has been seldom investigated at intraspecific level. We targeted Vipera ursinii ursinii, a meadow viper from southeastern France and central Italy, to assess determinants of transferability among geographically disjunct populations. We fitted HSMs upon occurrences of the Italian and French populations separately, as well as on the combined occurrence dataset. Internal transferability of HSMs, on spatially independent test data drawn from the calibration region, and their external transferability on the geographically disjunct populations were evaluated according to (a) use of full or spatially rarefied presence datasets; (b) ecology-driven or statistics-driven filtering of predictors; (c) modeling algorithm, testing generalized additive models and gradient boosting models; and (d) multivariate environmental novelty within test data. Niche overlap between French and Italian populations was also tested. Niche overlap was low, but niche divergence between the two populations' clusters was not corroborated. Nonetheless, wider niche breadth and heterogeneity of background environmental conditions characterizing the French populations led to low intercluster transferability. Although models fitted on the combined datasets did not attain consistently higher internal transferability than those separately fitted for the French and Italian populations, ensemble projection from the HSMs fitted on the joint occurrences produced more consistent suitability predictions across the full range of V. u. ursinii. Spatial thinning of occurrences ameliorated internal transferability but did not affect external transferability. The two approaches to predictors filtering did not differ in transferability of the respective HSMs but led to discrepant estimated environment-occurrence relationships and spatial predictions, while the two algorithms attained different relative rankings depending on the considered prediction task. Multivariate novelty of projection sites was negatively correlated to both internal transferability and external transferability. Our findings clarify issues researchers should keep in mind when using HSMs to get predictions across geographically disjunct populations.

Drivers of distributions and niches of North American cold-adapted amphibians: evaluating both climate and land use

Species distribution estimates are often used to understand the niche of a species; however, these are often based solely on climatic predictors. When the influences of biotic factors are ignored, erroneous inferences about range and niche may be made. We aimed to integrate climate data with a unique set of available land cover and land use data for the six cold-adapted amphibians of North America (Ambystoma macrodactylum, Anaxyrus hemiophrys, Anaxyrus boreas, Pseudacris maculata, Rana sylvatica, Rana luteiventris) to determine the relative importance of climate and non-climate drivers through the use of ecological niche models for present-day range estimates. We compared climate-only, land use-only, and combination models of climate and land use, derived from two different model selection techniques, to determine which was most likely to drive current distributions of cold-adapted amphibian species. Land use layers included land cover type, human population, vegetation type, ecoregion, and the overall human footprint. The most supported models included both climate and land use, with climate and human footprint variables having the highest permutation importance and percent contribution. Models that incorporated climate and land use data performed best as measured with AIC and AUC, although qualitatively most underestimated the northern range edge, implying potential sampling bias or locations of reduced habitat quality for these species in the northern area of the ranges. There were small differences in overall combination models dependent on the method of model selection. The overall effect sizes of landscape factors within the combination models were small except for one landscape feature: human footprint, which incorporated multiple aspects of anthropogenic change on the landscape, including human population density, travel access, and agricultural impact. This aspect of the landscape was just as important as climate, and counter to what we expected, the association was mostly positive, with a negative response only occurring at very high levels. This highlights the importance of moving beyond climate only species range estimates as land cover, specifically human impact, may be driving the patterns of species' ranges.

The failed invasion of Harmonia axyridis in the Azores, Portugal: Climatic restriction or wrong population origin?

We tested two questions: (i) whether the climatic conditions of the Azorean Islands in Portugal may have restricted the invasion of Harmonia axyridis across this archipelago and (ii) determine what population of this species could have a higher probability of invading the islands. We used MaxEnt to project the climate requirements of different H. axyridis populations from three regions of the world, and the potential global niche of the species in the Azorean islands. Then we assessed the suitability of the islands for each of the three H. axyridis populations and global potential niche through histograms analysis, Principal Component Analysis (PCA) of climate variables, and a variable-by-variable assessment of the suitability response curves compared with the climatic conditions of the Azores. Climatic conditions of the Azores are less suitable for the U.S. and native Asian populations of H. axyridis, and more suitable for European populations and the global potential niche. The PCA showed that the climatic conditions of the islands differed from the climatic requirements of H. axyridis. This difference is mainly explained by precipitation of the wettest month, isothermality, and the minimum temperature of the coldest month. We concluded that the climatic conditions of the Azores could have influenced the establishment and spread of H. axyridis on these islands from Europe. Our results showed that abiotic resistance represented by the climate of the potentially colonizable zones could hinder the establishment of invasive insects, but it could vary depending of the origin of the colonizing population.

The Relict Ecosystem of Maytenus senegalensis subsp. europaea in an Agricultural Landscape: Past, Present and Future Scenarios

Maytenus senegalensis subsp. europaea is a shrub belonging to the Celastraceae family, whose only European populations are distributed discontinuously along the south-eastern coast of the Iberian Peninsula, forming plant communities with great ecological value, unique in Europe. As it is an endangered species that makes up plant communities with great palaeoecological significance, the development of species distribution models is of major interest under different climatic scenarios, past, present and future, based on the fact that the climate could play a relevant role in the distribution of this species, as well as in the conformation of the communities in which it is integrated. Palaeoecological models were generated for the Maximum Interglacial, Last Maximum Glacial and Middle Holocene periods. The results obtained showed that the widest distribution of this species, and the maximum suitability of its habitat, occurred during the Last Glacial Maximum, when the temperatures of the peninsular southeast were not as contrasting as those of the rest of the European continent and were favored by higher rainfall. Under these conditions, large territories could act as shelters during the glacial period, a hypothesis reflected in the model’s results for this period, which exhibit a further expansion of M. europaea’s ecological niche. The future projection of models in around 2070, for four Representative Concentration Pathways according to the fifth report of the Intergovernmental Panel on Climate Change, showed that the most favorable areas for this species would be Campo de Dalías (southern portion of Almería province) as it presents the bioclimatic characteristics of greater adjustment to M. europaea’s ecological niche model. Currently, some of the largest specimens of the species survive in the agricultural landscapes in the southern Spain. These areas are almost totally destroyed and heavily altered by intensive agriculture greenhouses, also causing a severe fragmentation of the habitat, which implies a prospective extinction scenario in the near future.

On the importance of predictor choice, modelling technique, and number of pseudo-absences for bioclimatic envelope model performance

Bioclimatic envelope models are commonly used to assess the influence of climate change on species' distributions and biodiversity patterns. Understanding how methodological choices influence these models is critical for a comprehensive evaluation of the estimated impacts. Here we systematically assess the performance of bioclimatic envelope models in relation to the selection of predictors, modeling technique, and pseudo-absences. We considered (a) five different predictor sets, (b) seven commonly used modeling techniques and an ensemble model, and (c) three sets of pseudo-absences (1,000 pseudo-absences, 10,000 pseudo-absences, and the same as the number of presences). For each combination of predictor set, modeling technique, and pseudo-absence set, we fitted bioclimatic envelope models for 300 species of mammals, amphibians, and freshwater fish, and evaluated the predictive performance of the models using the true skill statistic (TSS), based on a spatially independent test set as well as cross-validation. On average across the species, model performance was mostly influenced by the choice of predictor set, followed by the choice of modeling technique. The number of the pseudo-absences did not have a strong effect on the model performance. Based on spatially independent testing, ensemble models based on species-specific nonredundant predictor sets revealed the highest predictive performance. In contrast, the Random Forest technique yielded the highest model performance in cross-validation but had the largest decrease in model performance when transferred to a different spatial context, thus highlighting the need for spatially independent model evaluation. We recommend building bioclimatic envelope models according to an ensemble modeling approach based on a nonredundant set of bioclimatic predictors, preferably selected for each modeled species.

Species distribution modelling of Bryde's whales, humpback whales, southern right whales, and sperm whales in the southern African region to inform their conservation in expanding economies

In southern African waters, information about species distribution and habitat preferences of many cetacean species is limited, despite the recent economic growth that may affect them. We determined the relative importance of eight environmental variables (bathymetry, distance to shore, slope, chlorophyll-a, salinity, eastwards sea water velocity, northwards sea water velocity and sea surface temperature) as drivers of seasonal habitat preferences of Bryde's whales (Balaenoptera brydei), humpback whales (Megaptera novaeangliae), southern right whales (Eubalaena australis) and sperm whales (Physeter macrocephalus). Using presence only data from multiple sources, we constructed predictive species distribution models (SDMs) consisting of ensembles of seven algorithms for these species during both summer and winter. Predicted distribution for all cetaceans was high in southern Africa and, in particular, within the South African Exclusive Economic Zone (EEZ). Predictive models indicated a more pronounced seasonal variation for humpback, sperm and southern right whales than for Bryde's whales. Southern right whales occurred closer to shore during winter, humpback whales were more likely to occur along the east coast in winter and the west coast in summer, and sperm whales were more concentrated off the shelf in winter. Our study shows that ensemble models using historical, incidental and scientific data, in conjunction with modern environmental variables, can provide baseline knowledge on important environmental drivers of cetacean distribution for conservation purposes. Results of this study can further be used to help develop marine spatial plans and identify important marine mammal areas.

What and where? Predicting invasion hotspots in the Arctic marine realm

The risk of aquatic invasions in the Arctic is expected to increase with climate warming, greater shipping activity and resource exploitation in the region. Planktonic and benthic marine aquatic invasive species (AIS) with the greatest potential for invasion and impact in the Canadian Arctic were identified and the 23 riskiest species were modelled to predict their potential spatial distributions at pan-Arctic and global scales. Modelling was conducted under present environmental conditions and two intermediate future (2050 and 2100) global warming scenarios. Invasion hotspots-regions of the Arctic where habitat is predicted to be suitable for a high number of potential AIS-were located in Hudson Bay, Northern Grand Banks/Labrador, Chukchi/Eastern Bering seas and Barents/White seas, suggesting that these regions could be more vulnerable to invasions. Globally, both benthic and planktonic organisms showed a future poleward shift in suitable habitat. At a pan-Arctic scale, all organisms showed suitable habitat gains under future conditions. However, at the global scale, habitat loss was predicted in more tropical regions for some taxa, particularly most planktonic species. Results from the present study can help prioritize management efforts in the face of climate change in the Arctic marine ecosystem. Moreover, this particular approach provides information to identify present and future high-risk areas for AIS in response to global warming.

Ecological niche models display nonlinear relationships with abundance and demographic performance across the latitudinal distribution of Astragalus utahensis (Fabaceae)

The potential for ecological niche models (ENMs) to accurately predict species' abundance and demographic performance throughout their geographic distributions remains a topic of substantial debate in ecology and biogeography. Few studies simultaneously examine the relationship between ENM predictions of environmental suitability and both a species' abundance and its demographic performance, particularly across its entire geographic distribution. Yet, studies of this type are essential for understanding the extent to which ENMs are a viable tool for identifying areas that may promote high abundance or performance of a species or how species might respond to future climate conditions. In this study, we used an ensemble ecological niche model to predict climatic suitability for the perennial forb Astragalus utahensis across its geographic distribution. We then examined relationships between projected climatic suitability and field-based measures of abundance, demographic performance, and forecasted stochastic population growth (λs). Predicted climatic suitability showed a J-shaped relationship with A. utahensis abundance, where low-abundance populations were associated with low-to-intermediate suitability scores and abundance increased sharply in areas of high predicted climatic suitability. A similar relationship existed between climatic suitability and λs from the center to the northern edge of the latitudinal distribution. Patterns such as these, where density or demographic performance only increases appreciably beyond some threshold of climatic suitability, support the contention that ENM-predicted climatic suitability does not necessarily represent a reliable predictor of abundance or performance across large geographic regions.

Predicting the Potential Current and Future Distribution of the Endangered Endemic Vascular Plant Primula boveana Decne. ex Duby in Egypt

Knowledge about population attributes, current geographic distribution, and changes over predicted climate change for many threatened endemic vascular plants is particularly limited in arid mountain environments. Primula boveana is one of the rarest and threatened plants worldwide, surviving exclusively in Saint Catherine Protectorate in the Sinaic biogeographic subsector of Egypt. This study aimed to define the current state of P. boveana populations, predict its current potential distribution, and use the best-model outputs to guide in field sampling and to forecast its future distribution under two climate change scenarios. The MaxEnt algorithm was used by relating 10 occurrence-points with different environmental predictors (27 bioclimatic, 3 topographic, and 8 edaphic factors). At the current knowledge level, the population size of P. boveana consists of 796 individuals, including 137 matures, distributed in only 250 m2. The Canonical Correlation Analysis (CCorA) displayed that population attributes (density, cover, size index, and plant vigor) were positively correlated with elevation, precipitation, and pH. Based on the best-fitting model, most predicted suitable central sites (69 km2) of P. boveana were located in the cool shaded high-elevated middle northern part of St. Catherine. Elevation, precipitation, temperature, and soil pH were the key contributors to P. boveana distribution in Egypt. After field trips in suitable predicted sites, we confirmed five extinct localities where P. boveana has been previously recorded and no new population was found. The projected map showed an upward range shift through the contraction of sites between 1800 and 2000 m and expansion towards high elevation (above 2000 m) at the southern parts of the St. Catherine area. To conserve P. boveana, it is recommended to initiate in situ conservation through reinforcement and reintroduction actions.

Shifts in potential geographical distribution of Pterocarya stenoptera under climate change scenarios in China

Climate change poses a serious threat to biodiversity. Predicting the effects of climate change on the distribution of a species' habitat can help humans address the potential threats which may change the scope and distribution of species. Pterocarya stenoptera is a common fast-growing tree species often used in the ecological restoration of riverbanks and alpine forests in central and eastern China. Until now, the characteristics of the distribution of this species' habitat are poorly known as are the environmental factors that influence its preferred habitat. In the present study, the Maximum Entropy Modeling (Maxent) algorithm and the Genetic Algorithm for Ruleset Production (GARP) were used to establish the models for the potential distribution of this species by selecting 236 sites with known occurrences and 14 environmental variables. The results indicate that both models have good predictive power. Minimum temperature of coldest month (Bio6), mean temperature of warmest quarter (Bio10), annual precipitation (Bio12), and precipitation of driest month (Bio14) were important environmental variables influencing the prediction of the Maxent model. According to the models, the temperate and subtropical regions of eastern China had high environmental suitability for this species, where the species had been recorded. Under each climate change scenario, climatic suitability of the existing range of this species increased, and its climatic niche expanded geographically to the north and higher elevation. GARP predicted a more conservative expansion. The projected spatial and temporal patterns of P. stenoptera can provide reference for the development of forest management and protection strategies.

Toward community predictions: Multi-scale modelling of mountain breeding birds' habitat suitability, landscape preferences, and environmental drivers

Across a large mountain area of the western Swiss Alps, we used occurrence data (presence-only points) of bird species to find suitable modelling solutions and build reliable distribution maps to deal with biodiversity and conservation necessities of bird species at finer scales. We have performed a multi-scale method of modelling, which uses distance, climatic, and focal variables at different scales (neighboring window sizes), to estimate the efficient scale of each environmental predictor and enhance our knowledge on how birds interact with their complex environment. To identify the best radius for each focal variable and the most efficient impact scale of each predictor, we have fitted univariate models per species. In the last step, the final set of variables were subsequently employed to build ensemble of small models (ESMs) at a fine spatial resolution of 100 m and generate species distribution maps as tools of conservation. We could build useful habitat suitability models for the three groups of species in the national red list. Our results indicate that, in general, the most important variables were in the group of bioclimatic variables including "Bio11" (Mean Temperature of Coldest Quarter), and "Bio 4" (Temperature Seasonality), then in the focal variables including "Forest", "Orchard", and "Agriculture area" as potential foraging, feeding and nesting sites. Our distribution maps are useful for identifying the most threatened species and their habitat and also for improving conservation effort to locate bird hotspots. It is a powerful strategy to improve the ecological understanding of the distribution of bird species in a dynamic heterogeneous environment.

Niche Modeling of Economically Important Mahanarva (Hemiptera, Cercopidae) Species in South and Central America: Are Brazilian Spittlebug Sugarcane Pests Potential Invaders of South and Central America?

Mahanarva fimbriolata, Mahanarva spectabilis, Mahanarva liturata, and Mahanarva posticata (Hemiptera: Cercopidae) are known pests in South American sugarcane and pasture plantations. They cause phytotoxicity by feeding directly from plant sap, greatly decreasing their production. In this work, we applied Species Distribution Modeling using the Maxent algorithm to analyze these four spittlebug species possible occurrence in South and Central America. Therefore, current and future bioclimatic variables, as well as elevation and other agricultural variables, were used within RStudio. Future climatic variables were differentiated between the years 2050 and 2070 with several representative concentration pathways. Overall, the species showed various suitable habitats in different countries of South and Central America. Nevertheless, when compared with future climate analysis, the number of suitable habitats is declining due to climate change. Elevation, isothermality, and different precipitation variables were mainly responsible for the results. We were able to analyze that spittlebug populations are not limited by temperature, but rather by other abiotic factors, such as precipitation.